Run transferable libraries - Learning functional bias in problem domains

Maarten Keijzer, Conor Ryan, Mike Cattolico

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This paper introduces the notion of Run Transferable Libraries, a mechanism to pass knowledge acquired in one GP run to another. We demonstrate that a system using these libraries can solve a selection of standard benchmarks considerably more quickly than GP with ADFs by building knowledge about a problem. Further, we demonstrate that a GP system with these libraries can scale much better than a standard ADF GP system when trained initially on simpler versions of difficult problems.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRiccardo Poli, Owen Holland, Wolfgang Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Dasgupta, Dario Floreano, James Foster, Mark Harman, Pier Luca Lanzi, Lee Spector, Andrea G. B. Tettamanzi, Dirk Thierens, Andrew M. Tyrrell
PublisherSpringer Verlag
Pages531-542
Number of pages12
ISBN (Print)3540223436
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3103
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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